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Record W4366828663 · doi:10.3390/en16093623

Experimental Development of Calcium Looping Carbon Capture Processes: An Overview of Opportunities and Challenges

2023· article· en· W4366828663 on OpenAlexaboutno aff
Rubens Coutinho Toledo, Gretta L.A.F. Arce, João Andrade de Carvalho, Ivonete Ávila

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsCalcium loopingContext (archaeology)ScopusFluidized bedCarbon capture and storage (timeline)European unionScale (ratio)Environmental scienceComputer sciencePolitical scienceClimate changeSorbentBusinessChemistryEngineeringWaste managementGeographyBiologyMEDLINE

Abstract

fetched live from OpenAlex

Global warming might be mitigated if emissions were interrupted through carbon capture technologies, as there is a significant amount of comprehensive studies on them. An outline of the main gaps and trends of a technology is critical for further development. In this context, this study provides an overview of calcium looping carbon capture processes that have proven their potential and commercial viability. A bibliometric analysis is conducted on both Scopus and Web of Science database by seeking the keywords “calcium looping”, “co2 capture”, and “fluidized bed” in titles, abstracts, and keywords. Word selection was based on a list of relevant papers on the topic. These items of data have been processed and analyzed based on the number of publications and citations by emphasizing recent publication evolution, journal influence, the use of specific keywords, and co-citation. Results reveal that the European Union (EU) leads the rankings on the topic, followed by Canada. Keyword choice might have affected the number of citations. Recent studies used limestone as a sorbent and a dual fluidized bed reactor with a calciner or resistance depending on its size. Most studies are focused on technology scale-up. Although scale-up seems to be a priority, multiple studies are designed to assess the effect of steam generation and SO2 on the process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.175
GPT teacher head0.299
Teacher spread0.124 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2023
Admission routes1
Has abstractyes

Explore more

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